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General info

Date - 05 Jun 2013 - 07 Jun 2013 

Location – Wageningen, the Netherlands

Keywords – RNA-seq, transcriptome, experimental design, statistics, data analysis, mapping, quantification, differential expression, interpretation, pathways

Organiser – Experimental Plant Sciences (EPS, Wageningen UR) & NBIC

Teacher(s)

Gabino Sanchez-Perez

Edouard Severing

Sandra Smit

Ole Madsen

Elio Schijlen

Contact(s)

Harm Nijveen

Patrick Koks

Sandra Smit

Description

The Power of RNA-seq is an introductory course for researchers who want to use RNA-seq in their research project.

The course will focus on questions like:

• Which questions can be addressed with RNA-seq?
• How many samples and replicates do I need?
• Which steps are involved in an RNA-seq experiment?
• What is Differential Expression?
• What can go wrong?

This is a 3-day course that will consist of lectures in the morning and extensive hands-on computer practicals in the afternoon. You’ll learn about all aspects of RNA-seq during the morning lectures on NGS & RNA-seq theory, but also the context, applicability, power and expected results of RNA-seq experiments. During the practicals, you’ll learn the basic steps an RNA-seq pipeline consist of, how to interpret your data and to put the results to use in your research project. We’ll use Galaxy, R and webtools for this.

topics:

• experiental design
• sequencing requirements
• steps in RNA-seq data analysis (data quality control, transcript identification, quantification, differential  expression, interpretation)

target audience:

Researchers in Life Sciences (‘biologists’) starting with application of NextGen Sequencing & RNA-seq.

• No previous NGS experience is needed
• No command line or Linux computer experience is required
• No R-experience is required

For more information, contact Patrick Koks

Incoming search terms:

• trinity transcriptome
• cuffdiff test statistics likelihood test
• Transcriptome seq
• tomato genomics
• RNAseq deduplication
• samseq workflow
• differential gene expression small sample size rna-seq
• tools available for differential expression
• RNA-Seq analysis BGI
• tomato genome size rnaseq

Forward genetic screens in model organisms are vital for identifying novel genes essential for developmental or disease processes. One drawback of these screens is the labor-intensive and sometimes inconclusive process of mapping the causative mutation.

In order to leverage high-throughput techniques to improve this mapping process, scientists at the University of Utah have developed a Mutation Mapping Analysis Pipeline for Pooled RNA-seq (MMAPPR) that works without parental strain information, without the requirement of a pre-existing snp map of the organism, and adapts to differential recombination frequencies across the genome. MMAPPR accommodates the considerable amount of noise in RNA-seq datasets, calculates allelic frequency by Euclidean distance followed by Loess regression analysis, identifies the region where the mutation lies and generates a list of putative coding region mutations in the linked genomic segment. MMAPPR can exploit RNA-seq datasets from isolated tissues or whole organisms that are utilized for gene expression and transcriptome analysis in novel mutants.

The researchers tested MMAPPR on two known mutant lines in zebrafish , nkx2.5 and tbx1, and used it to map two novel ENU-induced cardiovascular mutants, with mutations found in the ctr9 and cds2. MMAPPR can be directly applied to other model organisms, such as Drosophila and C. elegans, that are amenable to both forward genetic screens and pooled RNA-seq experiments. Thus, MMAPPR is a rapid, cost-efficient, and highly automated online pipeline, available to perform mutant mapping in any organism with a well assembled genome.

Availability – MMAPPR is available at: http://yost.genetics.utah.edu/software.php

• Hill JT, Demarest BL, Bisgrove BW, Gorsi B, Su YC, Yost HJ. (2013)  MMAPPR: Mutation Mapping Analysis Pipeline for Pooled RNA-seq. Genome Res [Epub ahead of print]. [abstract]

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• computing requirements for rna seq
• trim adaptors for rna-seq pipeline

Learn How to Analyze RNA-Seq Data

Learn about best practices using statistics to find biological meaning in your RNA-Seq analysis. In this webinar we will discuss:

- Statistical considerations for RNA-Seq
- Incorporating statistical robustness in your pathway and gene set analysis
- Basic steps of RNA-Seq analysis

We will highlight recent and innovative algorithms developed by Partek as well as cover the complete RNA-Seq data analysis workflow.

(register)

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• PARTEK SOFTWARE USER MANUAL
• how to analyze rna-seq data partek
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• partek reads fully overlap exon
• partek open source
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posted by bodhisattvax at Biostar

Hi all I’ve finally put together the results of the survey! First of all, thanks to everyone who participated – the response has been great, with 93 people completing the survey as of today.

The respondents have been a varied bunch, including all levels of academia (pre-docs, grad-students, pot-docs and PIs), core bioinformaticians and bioinformatics managers, as well as many from the industry. The majority of respondents appear to be based in the US and Europe but also in China, Korea and Australia.

I provide below my own summary of the survey’s findings, and I have a document which contains all the results, including all unedited comments. I’m not sure how I can upload this file on this site. If you would like it, please either check my post on seqanswers where I have been able to upload the file, or get in touch with me so I can email it to you. Biostars admins can you help here? Read more

Incoming search terms:

• cufflinks htseq count
• cufflinks normalize
• univ of chicago rnaseq workflow

A two-hour workshop offered in conjunction with the 2012 BG Retreat and featuring an introduction to next-gen sequencing and RNA-seq, demo on data processing in R, and hands-on RNA-seq analysis exercises

Get started processing raw Illumina data and learn how to get gene expression estimates, identify differentially expressed genes, and visualize your data — all this by practical class exercises! Read more

Incoming search terms:

• rnaseq exercise
• rna-seq analysis exercise

Duke Bioinformatics Workshop 2012 – Module 14: RNA‐Seq Analysis

Incoming search terms:

• bacteria RNA sequencing analysis pipeline
• rnaseq data images

Initially we asked: Do we yet have a firm handle on the most appropriate/accurate pipeline for analysis of RNA-Seq datasets?  Almost 90% of our readers said NO.  So in our last poll, we tried to dig a little deeper and asked: What is the biggest challenge when performing RNA-Seq data analysis?  See results below.  (N=100)

Check out or latest reader poll below in the right-hand sidebar and cast your vote!

Incoming search terms:

• find fusion transcripts from transcriptome assembly
• challenges in analysis of rnaseq data

This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies, including short read mapping, exon-exon splice junction detection, gene or isoform expression quantification, differential expression analysis and transcriptome reconstruction.

Table 1 Tools for short read mapping
Table 2 A list of software for splice junction detection
Table 3 Software for gene or isoform expression quantification
Table 4 Available tools for differential expression analysis
Table 5 Transcriptome reconstruction tools

• Chen G, Wang C, Shi T. (2011) Overview of available methods for diverse RNA-Seq data analyses. Sci China Life Sci 54(12), 1121-28. [article]

Previous reviews covering RNA-Seq data analysis strategies and tools:

June – Nature Methods
Sept -  Nature Reviews Genetics

Incoming search terms:

• rna-seq software
• Overview of available methods for diverse RNA-Seq data analyses
• RNAseq data analysis tools
• analysis of rna seq software
• rna sequencing software tools
• rna-seq analysis tools review
• rna-seq tools review

Making Sense of RNA-Seq Data

Course Duration
Course will run for four weeks from 02.01.2012 to 29.01.2012.

Course Material
Course material will be delivered online for each week at the start of the week. Register at http://www.labindia-gpod.com or write to uday@labindia-gpod.com

Registration
Send a copy of your biodata along with DD of Rs 5000/- in the name of Labindia Instruments Pvt. Ltd. payable at Thane to Co-ordinator, Labindia-GPOD, Swnand, Jivan Vihar Housing Society, SB Road, Pune or make online payment with debit/credit card using payment gateway.

Course Topics

1. Tools for mapping reads to reference genome or transcriptome
2. De novo assembly of Transcriptome
3. Finding isoforms and novel transcripts
4. Summarising mapped reads
5. Normalization and Differential Expression
6. Gene set and pathway enrichment

Incoming search terms:

• bgi rna seq
• rna-seq course 2012
• RNA-seq analysis Course
• online rna sequencing data
• rna seq online course
• rna sequencing course
• rnaseq analysis online
• rna-seq training course
• rna-seq course
• rna sequencing data analysis course

Hands-on training at EBI

Title: Advanced RNA-Seq and Chip-Seq Data Analysis
Date: 1-4 May 2012
Venue: EMBL-EBI, Hinxton, Nr Cambridge, CB10 1SD, UK
Organisers: Gabriella Rustici
Admin Support: Johanna Langrish

Incoming search terms:

• advanced rna-seq and chip-seq data analysis course

wapRNA  is a comprehensive web application tool for processing mRNA-seq and miRNA-seq data. This web tool includes four different modules: mRNA-seq and miRNA-seq sequenced from SOLiD or Solexa platform and all the modules were tested on previously published experimental data. It accepts raw sequence data with an optional reads filter, followed by mapping and gene annotation or miRNA prediction. wapRNA also integrates downstream functional analyses such as Gene Ontology, KEGG pathway, miRNA targets prediction and comparison of gene’s or miRNA’s different expression in different samples. Executable packages for installation on user’s local server are provided.

wapRNA is freely available for use at: http://waprna.big.ac.cn

• Zhao W, Liu W, Tian D, Tang B, Wang Y, Yu C, Li R, Ling Y, Wu J, Song S, Hu S.(2011) wapRNA: a web-based application for the processing of RNA sequences. Bioinformatics [Epub ahead of print]. [abstract]

Incoming search terms:

• waprna a web based application for the processing of rna sequences

SeqGene is an open-source software for mining next-gen sequencing datasets, focusing on post-alignment quality control, SNP and indel identification and annotation, RNA expression quantification, allele specific expression, and expression-genotying association analysis. SeqGene is especially suited for RNA-seq and exonome-seq applications, with focus on protein coding and regulatory regions of a genome. For RNA-seq applications, SeqGene implemented a novel topology-based pathway analysis method to identify SNP-Expression co-enrichment and SNP-Expression paths. Read more

Incoming search terms:

• RNA-seq_pipeline pdf
• general purpose software
• how many type of bios pdf
• 454 data analysis pipeline
• cut sequence mining software
• how to mine rnaseq data
• RNA seq for allele mining
• rnaseq datamine

Date:                  Aug 25-26, 2011
Location:         Amsterdam Medical Centre, Amsterdam
Organizer:       NBIC & LUMC
Contact(s):      Dr. Celia van Gelder
Level:               PhD

NBIC and LUMC will organize a 2-day course on RNA-seq data analysis on August 25 and 26, 2011. The course will be hosted by Antoine van Kampen at the AMC, Amsterdam. The course will consist of seminars and hands-on R practicals and will focus on data preprocessing, quality control, and statistical methods for detection of differential gene expression. It will be an expert course and a follow-up course on the general NBIC NGS data analysis course (which will be given from 5-7 september 2011 in Leiden. Participants for the RNA-seq course should preferably have participated in the general NGS course or otherwise have ample experience with NGS technology. The course is aimed at PhD students and postdocs, but scientific programmers with some background in biology and bioinformatics may also attend.

Course topics:

• RNA-seq experimental approaches
• Alignment
• Statistics for differential gene expression
• eQTL analysis R packages for RNA-seq data analysis

Confirmed speakers:

Rutger Brouwer, Lude Franke, Jelle Goeman, Philip de Groot, Peter-Bram ‘t Hoen (course coordinator), Antoine van Kampen, Nagesha Rao, Marieke Simonis, Marcel Willemsen, Kai Ye, Erik van Zwet

(more info… )

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• SEQanswers – RNA Sequencing

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• Biostar – RNA-Seq

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  • a simple question on RNA-Seq terminology
    This question may be very simple and basic, but I just need to confirm that I understand the differences among those terminologies in the RNA-Seq context. Suppose I have a sample called SLR, and it is sequenced on 5 lanes, so I have (among other output files) BAM files like L1_SLR, L2_SLR, L3_SLR, L5_SLR and L7_SLR.bam. Here, the letter "L" denotes […]
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    Hi, I have a bam file of all my accepted hits (tophat output) and an gtf file with my genes of interest for which I am trying to find potential antisense transcripts. I would like to create a list - preferably one that can be visualized in a genome browser - that shows all genes that have antisense reads in the accepted hits.bam file provided that there are […]
  • How to remove the intronic reads before counting
    I got RNASeq data in several samples. I checked the FastQC, seems the read quality are good (Hiseq 2000). But the problem is many reads are mapped to intronic region, and the regions have no any reference exons there (Refseq, ensembl, gencode). We don't know what they are. We guess the problem happend in library preparation, the concentration was low. N […]
  • Which strand of the mRNA molecule does the sequencer output as a "read"?
    In Illumina Stranded RNA-Seq (using the dUTP method), do the final reads in the fastq files correspond to the initial molecule (that was transcribed), or to the reverse complement of the molecule? C […]
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    If I were use cufflinks in de novo mode to find transcripts or genes in my data that did not align to known transcripts from UCSC or Ensembl, I wouldn't know what to do downstream of this. How would one go about confirming that these are indeed novel? What sort of validation steps would one take (computational or non-computational), what in-depth inform […]